Object-Respecting Color Image Segmentation

The problem of foreground/background segmentation is of great importance in image processing and computer vision. We present a novel Linear-Programming (LP)-based algorithm for color image segmentation. This algorithm segments an image into a conceptually-meaningful foreground region (usually corresponding to the object of interest) and background regions. From a few user specified strokes we learn two Gaussian Mixture models corresponding to the foreground and background region respectively. The algorithm performs well even when the object region consists of several different colors and textures. Due to the global optimality of LP, our algorithm is free from the drawback of getting into local minima.

[1]  Patrick Pérez,et al.  Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.

[2]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[3]  Ilan Shimshoni,et al.  Mean shift based clustering in high dimensions: a texture classification example , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Michael F. Cohen,et al.  An iterative optimization approach for unified image segmentation and matting , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[9]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.